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基于改进花授粉算法的航空发动机装配总体规划

章斌 卢洪义 宋汉强 刘舜 杨禹成 桑豆豆

章斌, 卢洪义, 宋汉强, 等. 基于改进花授粉算法的航空发动机装配总体规划[J]. 航空动力学报, 2024, 39(7):20220420 doi: 10.13224/j.cnki.jasp.20220420
引用本文: 章斌, 卢洪义, 宋汉强, 等. 基于改进花授粉算法的航空发动机装配总体规划[J]. 航空动力学报, 2024, 39(7):20220420 doi: 10.13224/j.cnki.jasp.20220420
ZHANG Bin, LU Hongyi, SONG Hanqiang, et al. Overall planning of aero-engine assembly based on improved flower pollination algorithm[J]. Journal of Aerospace Power, 2024, 39(7):20220420 doi: 10.13224/j.cnki.jasp.20220420
Citation: ZHANG Bin, LU Hongyi, SONG Hanqiang, et al. Overall planning of aero-engine assembly based on improved flower pollination algorithm[J]. Journal of Aerospace Power, 2024, 39(7):20220420 doi: 10.13224/j.cnki.jasp.20220420

基于改进花授粉算法的航空发动机装配总体规划

doi: 10.13224/j.cnki.jasp.20220420
基金项目: 江西省自然科学基金(20201BBE51002); 江西省研究生创新专项资金项目(YC2021-S685)
详细信息
    作者简介:

    章斌(1998-),男,硕士生,主要从事航空发动机数字装配的研究

    通讯作者:

    卢洪义(1965-),男,教授、博士生导师,博士,主要从事发动机智能设计的研究。E-mail:13964508115@163.com

  • 中图分类号: V263.2;TP391.9

Overall planning of aero-engine assembly based on improved flower pollination algorithm

  • 摘要:

    针对航空发动机结构复杂、零件数量多且装配效率低、装配成本高的问题,提出了一种改进花授粉算法(improved flower pollination algorithm, IFPA)的装配顺序优化方法。以装配优先性、装配稳定性、装配聚合性、装配重定向性和基础部件位置为影响因子构建优化目标评价体系,采用了不同的表示方案、反对立学习的初始种群生成、动态调整的转换概率,在全局授粉和局部授粉规则中引入了均匀变异和精英变异,并加入遗传突变。运用在航空发动机低压压气机装配规划上,验证了IFPA的有效性,并讨论了IFPA的参数影响,并同粒子群算法、遗传算法、蚁群算法和花授粉算法进行比较,该算法找到最优序列的概率分别提高了41%、42%、41%和20%,验证了IFPA在求解装配序列规划问题上的优越性。

     

  • 图 1  航空发动机的外部管路、附件

    Figure 1.  External piping and accessories of aero-engine

    图 2  航空发动机核心机图

    Figure 2.  Aero engine core diagram

    图 3  改进花授粉算法流程图

    Figure 3.  Flowchart of improved flower pollination algorithm

    图 4  航空发动机低压压气机

    Figure 4.  Aero engine low pressure compressor

    图 5  航空发动机低压压气机爆炸图

    Figure 5.  Explosion diagram of aero engine low pressure compressor

    图 6  压气机装配的IFPA 收敛图

    Figure 6.  IFPA convergence plot for compressor assembly

    图 7  5种算法平均适应度值收敛图

    Figure 7.  Convergence graph of average fitness value of 5 algorithms

    图 8  5种算法最优适应度值收敛图

    Figure 8.  Convergence graph of optimal fitness value of 5 algorithms

    表  1  低压压气机装配信息

    Table  1.   Low pressure compressor assembly information

    编号 零件名称 装配工具 方向
    1 整流罩 T3 +z
    2 螺母 T4 +z
    3 第1级压气机盘 T3 +z
    4 花键螺栓 T3 +z
    5 第2级压气机盘 T1 +z
    6 间隔衬套 T1 +z
    7 第3级压气机盘 T1 +z
    8 前涨圈座 T2 +z
    9 前轴承 T2 +z
    10 前间隔衬套 T2 +z
    11 主动齿轮 T2 +z
    12 后涨圈座 T2 +z
    13 低压压气机轴 T1 z
    14 中介支撑轴承螺帽 T4 z
    15 涨圈座 T3 z
    16 前中介轴承 T3 z
    17 中介轴承衬套 T3 z
    18 螺母 T4 z
    下载: 导出CSV

    表  2  不同参数对IFPA的影响

    Table  2.   Effect of different parameters on IFPA

    种群数 步长 平均值 次数 最优值
    10 1 9.5025 1 8.85
    9 9.495 1 8.85
    18 9.495 2 8.85
    20 1 9.435 4 8.85
    9 9.4175 5 8.85
    18 9.39 7 8.85
    50 1 9.32 12 8.85
    9 9.3275 11 8.85
    18 9.285 16 8.85
    80 1 9.23 24 8.85
    9 9.15 20 8.85
    18 9.175 20 8.85
    150 1 9.12 31 8.85
    9 9.075 37 8.85
    18 9.0775 33 8.85
    200 1 9.09 39 8.85
    9 9.04 43 8.35
    18 9.0675 40 8.85
    下载: 导出CSV

    表  3  5种算法100次对比结果

    Table  3.   100 comparison results of 5 algorithms

    参数 GA PSO ACO FPA IFPA
    最优适应度值 13.75 9.1 13.35 8.85 8.35
    最优适应度值平均值 18.95 10.96 18.31 9.23 9.04
    最优序列次数 1 2 1 23 43
    获最优的概率/% 1 2 1 23 43
    下载: 导出CSV

    表  4  5种算法获得的压气机最佳序列比较

    Table  4.   Comparison of compressor optimal sequences obtained by five algorithms

    算法 最优序列 最优
    适应度值
    可行性
    违规数
    装配稳定
    性数
    装配工具
    改变次数
    重定
    向次数
    基础
    部件值
    GA (13,14,7,6,5,15,12,11,9,8,
    4,16,3,10,2,1,17,18)
    13.75 3 19 12 7 0
    PSO (13,14,15,16,7,6,5,12,17,11,
    18,10,9,8,4,3,2,1)
    9.1 0 20 10 4 0
    ACO (13,14,16,6,7,15,5,17,11,18,
    12,10,9,8,4,3,2,1)
    13.35 3 20 12 7 0
    FPA (13,7,6,4,5,12,14,15,16,11,
    17,18,10,9,8,3,2,1)
    8.85 0 20 9 4 0
    IFPA (13,14,15,16,17,7,4,6,5,18,
    12,11,10,9,8,3,2,1)
    8.35 0 20 7 4 0
    下载: 导出CSV
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  • 收稿日期:  2022-06-13
  • 网络出版日期:  2023-12-25

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